NAME
   AI::ExpertSystem::Advanced - Expert System with backward, forward and
   mixed algorithms

DESCRIPTION
   Inspired in AI::ExpertSystem::Simple but with additional features:

   *   Uses backward, forward and mixed algorithms.

   *   Offers different views, so user can interact with the expert system
       via a terminal or with a friendly user interface.

   *   The knowledge database can be stored in any format such as YAML, XML
       or databases. You just need to choose what driver to use and you are
       done.

   *   Uses certainty factors.

SYNOPSIS
   An example of the mixed algorithm:

       use AI::ExpertSystem::Advanced;
       use AI::ExpertSystem::Advanced::KnowledgeDB::Factory;

       my $yaml_kdb = AI::ExpertSystem::Advanced::KnowledgeDB::Factory->new('yaml',
           {
               filename => 'examples/knowledge_db_one.yaml'
           });

       my $ai = AI::ExpertSystem::Advanced->new(
               viewer_class => 'terminal',
               knowledge_db => $yaml_kdb,
               initial_facts => ['I'],
               verbose => 1);
       $ai->mixed();
       $ai->summary();

Attributes
   initial_facts
       A list/set of initial facts the algorithms start using.

       During the forward algorithm the task is to find a list of goals
       caused by these initial facts (the only data we have in that
       moment).

       Lets imagine your knowledge database is about symptoms and diseases.
       You need to find what diseases are caused by the symptoms of a
       patient, these first symptons are the initial facts.

       Initial facts as also asked and inference facts can be negative or
       positive. By default the initial facts are positive.

       Keep in mind that the data contained in this array can be the IDs or
       the name of the fact.

       This array will be converted to initial_facts_dict. And all the data
       (ids or or names) will be made of only IDs.

           my $ai = AI::ExpertSystem::Advanced->new(
                   viewer_class => 'terminal',
                   knowledge_db => $yaml_kdb,
                   initial_facts => ['I', ['F', '-'], ['G', '+']);

       As you can see if you want to provide the sign of a fact, just
       *encapsulate* it in an array, the first item should be the fact and
       the second one the sign.

   initial_facts_dict
       This dictionary (see AI::ExpertSystem::Advanced::Dictionary has the
       sasme data of initial_facts but with the additional feature(s) of
       proviing iterators and a quick way to find elements.

   goals_to_check
           my $ai = AI::ExpertSystem::Advanced->new(
                   viewer_class => 'terminal',
                   knowledge_db => $yaml_kdb,
                   goals_to_check => ['J']);

       When doing the backward() algorithm it's required to have at least
       one goal (aka hypothesis).

       This could be pretty similar to initial_facts, with the difference
       that the initial facts are used more with the causes of the rules
       and this one with the goals (usually one in a well defined knowledge
       database).

       The same rule of initial_facts apply here, you can provide the sign
       of the facts and you can provide the id or the name of them.

       From our example of symptoms and diseases lets imagine we have the
       hypothesis that a patient has flu, we don't know the symptoms it
       has, we want the expert system to keep asking us for them to make
       sure that our hypothesis is correct (or incorrect in case there's
       not enough information).

   goals_to_check_dict
       Very similar to goals_to_check (and indeed of initial_facts_dict).
       We want to make the job easier.

       It will be a dictionary made of the data of goals_to_check.

   inference_facts
       Inference facts are basically the core of an expert system. These
       are facts that are found and copied when a set of facts (initial,
       inference or asked) match with the causes of a goal.

       inference_facts is a AI::ExpertSystem::Advanced::Dictionary, it will
       store the name of the fact, the rule that caused these facts to be
       copied to this dictionary, the sign and the algorithm that triggered
       it.

   knowledge_db
       The object reference of the knowledge database
       AI::ExpertSystem::Advanced is using.

   asked_facts
       During the backward() algorithm there will be cases when there's no
       clarity if a fact exists. In these cases the backward() will be
       asking the user (via automation or real questions) if a fact exists.

       Going back to the initial_facts example of symptoms and diseases.
       Imagine the algorithm is checking a rule, some of the facts of the
       rule make a match with the ones of initial_facts or inference_facts
       but some wont, for these *unsure* facts the backward() will ask the
       user if a symptom (a fact) exists. All these asked facts will be
       stored here.

   visited_rules
       Keeps a record of all the rules the algorithms have visited and also
       the number of causes each rule has.

   verbose
           my $ai = AI::ExpertSystem::Advanced->new(
                   viewer_class => 'terminal',
                   knowledge_db => $yaml_kdb,
                   initial_facts => ['I'],
                   verbose => 1);

       By default this is turned off. If you want to know what happens
       behind the scenes turn this on.

       Everything that needs to be debugged will be passed to the debug()
       method of your viewer.

   viewer
       Is the object AI::ExpertSystem::Advanced will be using for printing
       what is happening and for interacting with the user (such as asking
       the asked_facts).

       This is practical if you want to use a viewer object that is not
       provided by AI::ExpertSystem::Advanced::Viewer::Factory.

   viewer_class
       Is the the class name of the viewer.

       You can decide to use the viewers
       AI::ExpertSystem::Advanced::Viewer::Factory offers, in this case you
       can pass the object or only the name of your favorite viewer.

   found_factor
       In your knowledge database you can give different *weights* to the
       facts of each rule (eg to define what facts have more *priority*).
       During the mixed() algorithm it will be checking what causes are
       found in the inference_facts and in the asked_facts dictionaries,
       then the total number of matches (or total number of certainity
       factors of each rule) will be compared versus the value of this
       factor, if it's higher or equal then the rule will be triggered.

       You can read the documentation of the mixed() algorithm to know the
       two ways this factor can be used.

   shot_rules
       All the rules that are shot are stored here. This is a hash, the key
       of each item is the rule id while its value is the precision time
       when the rule was shot.

       The precision time is useful for knowing when a rule was shot and
       based on that you can know what steps it followed so you can compare
       (or reproduce) them.

Constants
   * FACT_SIGN_NEGATIVE
       Used when a fact is negative, aka, a fact doesn't happen.

   * FACT_SIGN_POSITIVE
       Used for those facts that happen.

   * FACT_SIGN_UNSURE
       Used when there's no straight answer of a fact, eg, we don't know if
       an answer will change the result.

Methods
 shoot($rule, $algorithm)
   Shoots the given rule. It will do the following verifications:

   *   Each of the facts (causes) will be compared against the
       initial_facts_dict, inference_facts and asked_facts (in this order).

   *   If any initial, inference or asked fact matches with a cause but
       it's negative then all of its goals (usually only one by rule) will
       be copied to the inference_facts with a negative sign, otherwise a
       positive sign will be used.

   *   Will add the rule to the shot_rules hash.

 is_rule_shot($rule)
   Verifies if the given $rule has been shot.

 get_goals_by_rule($rule)
   Will ask the knowledge_db for the goals of the given $rule.

   A AI::ExpertSystem::Advanced::Dictionary will be returned.

 get_causes_by_rule($rule)
   Will ask the knowledge_db for the causes of the given $rule.

   A AI::ExpertSystem::Advanced::Dictionary will be returned.

 is_fact_negative($dict_name, $fact)
   Will check if the given $fact of the given dictionary ($dict_name) is
   negative.

 copy_to_inference_facts($facts, $sign, $algorithm, $rule)
   Copies the given $facts (a dictionary, usually goal(s) of a rule) to the
   inference_facts dictionary. All the given goals will be copied with the
   given $sign.

   Additionally it will add the given $algorithm and $rule to the inference
   facts. So later we can know how we got to a certain inference fact.

 compare_causes_with_facts($rule)
   Compares the causes of the given $rule with:

   *   Initial facts

   *   Inference facts

   *   Asked facts

   It will be couting the matches of all of the above dictionaries, so for
   example if we have four causes, two make match with initial facts, other
   with inference and the remaining one with the asked facts, then it will
   evaluate to true since we have a match of the four causes.

 get_causes_match_factor($rule)
   Similar to compare_causes_with_facts() but with the difference that it
   will count the "match factor" of each matched cause and return the total
   of this weight.

   The match factor is used by the mixed() algorithm and is useful to know
   if a certain rule should be shoot or not even if not all of the causes
   exist in our facts.

   The *match factor* is calculated in two ways:

   *   Will do a sum of the weight for each matched cause. Please note that
       if only one cause of a rule has a specified weight then the
       remaining causes will default to the total weight minus 1 and then
       divided with the total number of causes (matched or not) that don't
       have a weight.

   *   If no weight is found with all the causes of the given rule, then
       the total number of matches will be divided by the total number of
       causes.

 is_goal_in_our_facts($goal)
   Checks if the given $goal is in:

   1   The initial facts

   2   The inference facts

   3   The asked facts

 remove_last_ivisited_rule()
   Removes the last visited rule and return its number.

 visit_rule($rule, $total_causes)
   Adds the given $rule to the end of the visited_rules.

 copy_to_goals_to_check($rule, $facts)
   Copies a list of facts (usually a list of causes of a rule) to
   goals_to_check_dict.

   The rule ID of the goals that are being copied is also stored in the
   hahs.

 ask_about($fact)
   Uses viewer to ask the user for the existence of the given $fact.

   The valid answers are:

   + or FACT_SIGN_POSITIVE
       In case user knows of it.

   - or FACT_SIGN_NEGATIVE
       In case user doesn't knows of it.

   ~ or FACT_SIGN_UNSURE
       In case user doesn't have any clue about the given fact.

 get_rule_by_goal($goal)
   Looks in the knowledge_db for the rule that has the given goal. If a
   rule is found its number is returned, otherwise undef.

 forward()
       use AI::ExpertSystem::Advanced;
       use AI::ExpertSystem::Advanced::KnowledgeDB::Factory;

       my $yaml_kdb = AI::ExpertSystem::Advanced::KnowledgeDB::Factory->new('yaml',
               {
                   filename => 'examples/knowledge_db_one.yaml'
               });

       my $ai = AI::ExpertSystem::Advanced->new(
               viewer_class => 'terminal',
               knowledge_db => $yaml_kdb,
               initial_facts => ['F', 'J']);
       $ai->forward();
       $ai->summary();

   The forward chaining algorithm is one of the main methods used in Expert
   Systems. It starts with a set of variables (known as initial facts) and
   reads the available rules.

   It will be reading rule by rule and for each one it will compare its
   causes with the initial, inference and asked facts. If all of these
   causes are in the facts then the rule will be shoot and all of its goals
   will be copied/converted to inference facts and will restart reading
   from the first rule.

 backward()
       use AI::ExpertSystem::Advanced;
       use AI::ExpertSystem::Advanced::KnowledgeDB::Factory;

       my $yaml_kdb = AI::ExpertSystem::Advanced::KnowledgeDB::Factory->new('yaml',
           {
               filename => 'examples/knowledge_db_one.yaml'
               });

       my $ai = AI::ExpertSystem::Advanced->new(
               viewer_class => 'terminal',
               knowledge_db => $yaml_kdb,
               goals_to_check => ['J']);
       $ai->backward();
       $ai->summary();

   The backward algorithm starts with a set of *assumed* goals (facts). It
   will start reading goal by goal. For each goal it will check if it
   exists in the initial, inference and asked facts (see
   is_goal_in_our_facts()) for more information).

   *   If the goal exist then it will be removed from the dictionary, it
       will also verify if there are more visited rules to shoot.

       If there are still more visited rules to shoot then it will check
       from what rule the goal comes from, if it was copied from a rule
       then this data will exist. With this information then it will see
       how many of the causes of this given rule are still in the
       goals_to_check_dict.

       In case there are still causes of this rule in goals_to_check_dict
       then the amount of causes pending will be reduced by one. Otherwise
       (if the amount is 0) then the rule of this last removed goal will be
       shoot.

   *   If the goal doesn't exist in the mentioned facts then the goal will
       be searched in the goals of every rule.

       In case it finds the rule that has the goal, this rule will be
       marked (added) to the list of visited rules (visited_rules) and also
       all of its causes will be added to the top of the
       goals_to_check_dict and it will start reading again all the goals.

       If there's the case where the goal doesn't exist as a goal in the
       rules then it will ask the user (via ask_about()) for the existence
       of it. If user is not sure about it then the algorithm ends.

 mixed()
   As its name says, it's a mix of forward() and backward() algorithms, it
   requires to have at least one initial fact.

   The first thing it does is to run the forward() algorithm (hence the
   need of at least one initial fact). If the algorithm fails then the
   mixed algorithm also ends unsuccessfully.

   Once the first *run* of forward() algorithm happens it starts looking
   for any positive inference fact, if only one is found then this ends the
   algorithm with the assumption it knows what's happening.

   In case no positive inference fact is found then it will start reading
   the rules and creating a list of intuitive facts.

   For each rule it will get a *certainty factor* of its causes versus the
   initial, inference and asked facts. In case the certainity factor is
   greater or equal than found_factor then all of its goals will be copied
   to the intuitive facts (eg, read it as: it assumes the goals have
   something to do with our first initial facts).

   Once all the rules are read then it verifies if there are intuitive
   facts, if no facts are found then it ends with the intuition, otherwise
   it will run the backward() algorithm for each one of these facts (eg,
   each fact will be converted to a goal). After each *run* of the
   backward() algorithm it will verify for any positive inference fact, if
   just one is found then the algorithm ends.

   At the end (if there are still no positive inference facts) it will run
   the forward() algorithm and restart (by looking again for any positive
   inference fact).

   A good example to understand how this algorithm is useful is: imagine
   you are a doctor and know some of the symptoms of a patient. Probably
   with the first symptoms you have you can get to a positive conclusion
   (eg that a patient has *X* disease). However in case there's still no
   clue, then a set of questions (done by the call of backward()) of
   symptons related to the initial symptoms will be asked to the user. For
   example, we know that that the patient has a headache but that doesn't
   give us any positive answer, what if the patient has flu or another
   disease? Then a set of these *related* symptons will be asked to the
   user.

 summary($return)
   The main purpose of any expert system is the ability to explain: what is
   happening, how it got to a result, what assumption(s) it required to
   make, the fatcs that were excluded and the ones that were used.

   This method will use the viewer (or return the result) in YAML format of
   all the rules that were shot. It will explain how it got to each one of
   the causes so a better explanation can be done by the viewer.

   If $return is defined (eg, it got any parameter) then the result wont be
   passed to the viewer, instead it will be returned as a string.

SEE ALSO
   Take a look AI::ExpertSystem::Simple too.

AUTHOR
   Pablo Fischer ([email protected]).

COPYRIGHT
   Copyright (C) 2010 by Pablo Fischer.

   This library is free software; you can redistribute it and/or modify it
   under the same terms as Perl itself.

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